A pre-flight checklist for standing up your first media mix model

A pre-flight checklist for standing up your first media mix model

The question: cookies are eroding and you want a measurement approach that doesn't depend on user-level tracking. Media mix modeling (MMM) — regressing aggregate sales against aggregate spend — is the answer, but what do you need before you fit anything?

The data readiness checklist:

— At least 2-3 years of weekly data, or ~104+ rows. MMM is hungry; thin histories overfit.
— Spend and impressions per channel. Spend alone confounds price changes with volume changes.
— Control variables: seasonality, price, promotions, distribution, competitor activity. Omit these and the model hands their effect to whatever channel happens to correlate.
— Variation in spend. If a channel ran at a flat budget for two years, the model literally cannot estimate its slope.

The modeling checklist:

— Apply adstock (carryover) and saturation (diminishing returns) curves — Google's open-source Meridian and Meta's Robyn both bake these in.
— Validate out-of-sample, not just on training fit. A high R-squared on history proves nothing about prediction.
— Sanity-check coefficients against geo experiments. Calibrated MMM is the current frontier for exactly this reason.

The nuance: MMM estimates correlation under a structural assumption. Without spend variation or experimental calibration, a confident-looking coefficient can be pure confounding.

Bottom line for practitioners: gather long, granular, well-controlled data first; model second; calibrate against a holdout always. An uncalibrated MMM is a hypothesis, not a measurement.